Practical Deep Learning with PyTorch
Learn how to build and deploy Deep Neural Networks to solve common Machine Learning problems
Instructor: Joe Papa, O'Reilly Author & Trainer
What you will learn:
This two-day course will teach you how to design, train, and deploy Deep Neural Networks using Python and PyTorch. Your course starts with learning how to represent data with PyTorch tensors. You’ll then develop ML models using DNN to solve problems like image classification and text analysis using CNN and other types of NN architectures. This course is hands-on and you’ll be executing code in Google Colab on both CPU and GPU processors.
During this course, you will:
- Understand how Deep Neural Networks work
- Apply Machine Learning methods to create Deep Learning models
- Discover popular existing DL models and how to leverage them for your own purpose
- Review DL architectures like CNN, GAN, RNN, and Transformers and when to use them
- Discover the common problems in Deep Learning and how to overcome them
- Understand why you should use Batch Normalization, Dropout, or different Activation functions
- Learn how to solve problems like:
- Image Classification
- Object Detection
- NLP Sentiment Analysis
- Learn how to apply DL methods to your own work
Become a PyTorch expert in designing, training, and deploying DL models
This course is for you, if:
You’re a student, developer, data scientist, researcher, or engineer interested in learning how to use Deep Learning and AI to solve problems. You’re eager to build your skills in one of the most popular DL frameworks, PyTorch, and you want to understand how you can apply Machine Learning to your own work. In addition, you may also want to complete side projects using Deep Learning to build your experience, advance your career or land a new job in AI.
Review Machine Learning Process & Python for Machine Learning
Coding Exercise: Tensors
Coding Exercise: Custom Datasets
Solving ML problems with existing NN
Coding Exercise: Image Classification
Deep Learning Architectures
Common DL Problems
Designing Deep Networks
Coding Exercise: Designing a CNN
Coding Exercise: Training NN for Text Analysis
Certificate of Completion
Print a copy for your wall or post your certificate on social media sites like LinkedIn.
Earn 15 hours of training towards your continuing education goals.
You'll receive a complete workbook for class participation and future reference.
We'll continue to send you optional coding challenges after the course so you can continue to practice.
Meet Your Instructor
Joe Papa has helped over 6,000 students build their skills in Deep Learning and AI. He holds an MSEE, 25+ years experience in R&D, and has led AI Teams at Booz Allen, Perspecta Labs, and Mobile Insights. Joe has published two books on PyTorch and Deep Learning with O'Reilly and has published multiple online courses with Udemy and Packt Publishing.